Methods and systems employing infrared thermography for defect detection and analysis

ABSTRACT

Described are methods and systems for providing improved defect detection and analysis using infrared thermography. Test vectors heat features of a device under test to produce thermal characteristics useful in identifying defects. The test vectors are timed to enhance the thermal contrast between defects and the surrounding features, enabling IR imaging equipment to acquire improved thermographic images. In some embodiments, a combination of AC and DC test vectors maximize power transfer to expedite heating, and therefore testing. Mathematical transformations applied to the improved images further enhance defect detection and analysis. Some defects produce image artifacts, or “defect artifacts,” that obscure the defects, rendering difficult the task of defect location. Some embodiments employ defect-location algorithms that analyze defect artifacts to precisely locate corresponding defects.

BACKGROUND

[0001] Electrical circuits, such as printed circuit boards (PCBs),integrated circuits, and flat-panel displays (FPDs), may be tested fordefects using infrared (IR) thermography. In general, power is appliedto a device under test (DUT) to heat various of the device features. Aninfrared detector then captures a test image of the heated DUT. Theresulting image, a collection of pixel-intensity values spatiallycorrelated to the imaged object, is then compared with a similarcollection of reference image data. Differences between the test andreference data, typically stored as a “composite image,” indicate thepresence of defects.

[0002] Defect identification algorithms analyze composite images toautomatically identify defects, and consequently improve throughput andquality in device manufacturing. Examples of such inspection systemsinclude, but are not limited to, inspection of FPDs, PCBs, MEMS-baseddevices, semiconductor devices, and biomedical specimen. One purpose ofsuch systems is to test for defects potentially present on a device atsome critical point during manufacture of that device. Once identified,the defects can then be repaired by a repair system, or a choice can bemade to reject the device, leading to manufacturing cost savings in bothcases. Other applications include inspection and identification ofartifact-like features in research specimens, i.e. in biology.

[0003] One particularly important use of IR thermography is the testingof the active layer, or “active plate,” of liquid-crystal display (LCD)panels. Defect analysis can be used to improve processing and increasemanufacturing yield. Also important, defective panels can be repaired,provided the number and extent of defects is not too great, againincreasing manufacturing yield.

[0004]FIG. 1 (prior art) depicts portions of an active plate 100 for usewith an LCD panel. (FIG. 1 was taken from U.S. Pat. No. 6,111,424 toBosacchi, which issued Aug. 29, 2000, and is incorporated herein byreference.) Active plate 100 includes a first shorting bar 105 connectedto each pixel in an array of pixels 110 via a collection of source lines115 and a second shorting bar 120 connected to each pixel 110 via acollection of gate lines (control lines) 125.

[0005] According to Bosacchi, active plate 100 is tested by evaluatingthe IR emission of active plate 100 with voltage applied to shortingbars 105 and 120. With power thus applied, portions of plate 100 operateas resistive circuits, and consequently dissipate heat. The heatingresponse characteristics of plate 100 are then evaluated, preferablyafter plate 100 reaches a stable operating temperature (thermalequilibrium).

[0006] In the absence of defects, the pixel array should heat upuniformly. Non-uniform thermal characteristics, identified as aberrantIR intensity values, therefore indicate the presence of defects.Reference intensity values can be, obtained by averaging the pixelintensity values of a given image frame, or by means of a referenceframe corresponding to an ideal or defect-free reference plate.

[0007]FIG. 2 (prior art) details a portion of a conventional pixel 110,and is used here to illustrate a number of potential defects. Thedepicted features of pixel 110 are associated with the active plate of aliquid-crystal display, and include a thin-film transistor 200 having afirst current-handling terminal connected to one of source lines 115, acontrol terminal connected to one of gate lines 125, and a secondcurrent-handling terminal connected to a capacitor 210. The secondelectrode of capacitor 210 connects to a common line 212. Pixel 110 alsoincludes a second capacitor 211 having a liquid-crystal dielectric.

[0008] The defects, which are illustrative and not exhaustive, includeboth shorts and opens. The shorts are between: source line 115 and gateline 125 (short 215) or common line 212 (short 216); the twocurrent-handling terminals of transistor 200 (short 220); the gate andsecond current-handling terminal of transistor 200 (short 225); and thetwo terminals of capacitor 210 (short 226). The opens segment thesource, gate, and common lines (opens 227, 228, and 229), and arebetween: source line 115 and transistor 200 (open 230), gate line 125and the control terminal of transistor 200 (open 232), capacitor 210 andcommon line 212 (open 235), and transistor 200 and capacitor 210 (open233).

[0009] Each defect of FIG. 2, plus a number of others, adversely impactsthe operation of pixel 110. Unfortunately, many of these defects aredifficult to discover using conventional test methods. There istherefore a need for improved methods and systems for identifying andlocating defects.

[0010] Some inspection systems include an excitation source that excitesthe object under test in a way that highlights defects to an imagingsystem. The type of excitation depends upon the imaging system, whichmay acquire images based on visible light, infrared, combinedspectroscopy, magnetic fields, etc. Whatever imaging system is employed,test images of the object under test are contrasted with some referenceimage to obtain a composite image: significant differences between thetest and reference images show up in the composite image, and identifypotential defects.

[0011] Some forms of excitation produce defect artifacts, which aredifferences between test and reference images that are caused by defectsbut that do not physically correlate to defect areas. A short betweentwo lines increases current through those lines, and consequentlyelevates the temperatures of the lines along with the short. Thus thelines, though not themselves defective, nevertheless appear with theshort in the composite image. The defect data representing the short isthus imbedded within defect-artifact data (i.e., a “defect artifact”).Defect artifacts often obscure the associated defects, rendering themdifficult to precisely locate. Human operators can locate a defectwithin a defect artifact by careful study under a microscope, but peopleare relatively slow and are quickly fatigued. There is therefore a needfor means of automatically distinguishing defects from their relatedartifacts.

BRIEF DESCRIPTION OF THE FIGURES

[0012]FIG. 1 (prior art) depicts portions of an active plate 100 for usewith an LCD panel.

[0013]FIG. 2 (prior art) details an exemplary pixel 110, and is usedhere to illustrate a number of potential defects.

[0014]FIG. 3 depicts a test configuration 300, including a conventionalpanel 305 and an inspection system adapted in accordance with anembodiment of the invention.

[0015]FIG. 4 depicts a portion of a panel 400 adapted in accordance withone embodiment to provide enhanced testability.

[0016]FIG. 5 depicts a portion of an LCD panel 500 adapted in accordancewith one embodiment.

[0017]FIG. 6A is a diagram 600 illustrating the thermal response of theabove-described sample defect and the surrounding area.

[0018]FIG. 6B depicts a diagram 630 illustrating the thermal response ofsample defect 610.

[0019]FIG. 6C depicts an experimentally obtained image 680 highlightinga line 685 indicative of an open.

[0020]FIG. 7 depicts a composite image 700 showing three representativedefects, a point-type defect 705, a line-type defect 707, and acorner-type defect 710.

[0021]FIG. 8 is a composite image 800 exhibiting a point-type defect805, a line-type defect 810, and a corner-type defect 815.

[0022]FIG. 9 is a flowchart 900 depicting a defect-location algorithm900 in accordance with one embodiment.

[0023]FIG. 10 depicts an embodiment of MFA 910 of FIG. 9.

[0024]FIG. 11 depicts an embodiment of type-specific MFA 935 of FIG. 9adapted for use with line-type defect artifacts.

[0025]FIG. 12 depicts an embodiment of defect-location algorithm (DLA)945 of FIG. 9.

[0026]FIG. 13 depicts an array of LDF structures 1205 in accordance withone embodiment.

[0027]FIG. 14 depicts an illustrative filtered composite image 1400similar to what one might expect from step 907 of FIG. 9.

[0028] FIGS. 15A-15D depict binary, type-specific images 1505, 1510, and1515 like images 940[1 . . . i] of FIG. 9.

[0029]FIG. 16 depicts a tree 1600, a compilation of the information inkeys 1525, 1530, and 1535 of FIGS. 15B-D.

[0030]FIG. 17 is an illustrative peak profile 1700 showing therelationship between four defect artifacts 1705, 1710, 1715, and 1720.

DETAILED DESCRIPTION

[0031]FIG. 3 depicts a test configuration 300, including a conventionalpanel 305 and an inspection system 310 adapted in accordance with anembodiment of the invention. Panel 305 is similar to panel 100 of FIGS.1 and 2, like-numbered elements being the same or similar. Panel 305includes a shorting bar 312 that is not depicted in FIG. 1, but isnevertheless conventional. Inspection system 310 includes an IR detector315 (e.g., an IR camera) oriented over panel 305 to provide image datato a computer 320 via a frame grabber 325. An excitation source, signalgenerator 330, provides electrical test signals, or “test vectors,” topanel 310. The test vectors heat features of panel 310 to producethermal characteristics useful in identifying defects.

[0032] Computer 325 controls signal generator 330 to apply test vectorsto panel 305. These test vectors enhance the thermal contrast betweendefects and the surrounding features, and consequently allow IR detector315 to acquire improved thermographic images for defect detection andanalysis. Computer 320 additionally instructs IR detector 315 when toacquire image data, receives and processes captured test-image data fromframe grabber 325, and provides a user interface (not shown).

[0033] IR detector 315 should have excellent temperature sensitivity. Inone embodiment, detector 315 is an IR Focal-Plane Array Thermal ImagingCamera employing a 256×320 element InSb (Indium Antimonide) detector.The minimum temperature sensitivity of this camera is less than 0.020degrees C. Some embodiments include multiple IR detectors, for example arelatively low magnification IR camera for defect detection and a highermagnification IR camera for defect detection and analysis. Additionalcameras might also be used to increase inspection area, and thusinspection bandwidth.

[0034] Signal generator 330 provides a source test vector V_(TS) toshorting bar 105, a gate test vector V_(TG) to shorting bar 120, and acommon test vector V_(TC) to shorting bar 312. Referring back to FIG. 2,testing for some types of defects (e.g., opens 230, 233, and 235)requires transistor 200 be turned on to create a signal path betweenrespective source and common lines 115 and 212. Signal generator 330thus applies a DC test vector V_(TG) to gate line 125 (via shorting bar120), thus turning transistor 200 on, while applying the source andcommon test vectors V_(TS) and V_(TC).

[0035] Even with transistor 200 forward biased, a non-defective pixel110 will not pass direct current, absent short 226, because capacitor210 blocks direct current. The source and common test vectors V_(TS) andV_(TC) are therefore selected to produce an AC signal that passesthrough capacitor 210. The frequency of the AC signal is matched to theimpedance of the load provided by panel 305 to maximize power transferto panel 305, which expedites heating, and therefore testing. Maximizingpower transfer also allows for testing with lower applied voltages,which are less likely to damage sensitive components. Also important, asdetailed below, a combination of faster heating and specificimage-capture timing provides improved thermal contrast. In oneembodiment, source test vector V_(TS) oscillates from zero to 30 voltsat about 70 KHz and common test vector V_(TC) is ground potential.

[0036] Some embodiments apply either AC or DC test vectors betweensource line 115 and common line 212, source line 115 and gate line 125,and between gate line 125 and common line 212. Still other embodimentemploy AC signals to turn transistor 200 on. The simultaneousapplication of AC and DC test vectors, as detailed above, facilitatesmore comprehensive testing than is obtained by application of only onetype of waveform (e.g., only DC, AC, or pulsed DC test vectors).

[0037]FIG. 4 depicts a portion of a panel 400 adapted in accordance withone embodiment to provide enhanced testability. Panel 400 conventionallyincludes an array of pixels 405, each connected to a source line 410, agate line 415, and a common line 420. Four sets of shorting bars (sourcebars 425, gate bars 435, and common bars 430) allow inspection systems,such as that of FIG. 3, to test subsets of pixels 405. Alternatively,four sets of one type of bar (e.g., four source bars or four gate bars)can be used to energize selected columns or rows. Energizing somefeatures while leaving adjacent features de-energized can improve imagecontrast. In other embodiments, only one or two types of shorting barsare provided in sets. There may only be one gate bar 435 and one commonbar 430, for example, in which case pixels 405 can be excited in foursets of columns. Moreover, one or more sets of shorting bars can includemore or fewer than four shorting bars.

[0038]FIG. 5 depicts a portion of an LCD panel 500 adapted in accordancewith another embodiment. Panel 500 conventionally includes an array ofpixels 505, each connected to a source line 510, a gate line 515, and acommon line 520. Source bars 525, gate bars 530, and common bars 535 aresegmented to allow an inspection system to power test areas (e.g., area540) one at a time. Alternatively, fewer types of bars need besegmented. For example, the common bar need not be segmented to powerarea 540 if the source and gate bars are segmented. Area 540 might becoextensive with the field of view of the IR detector used to captureimages. The number of pixels 505 in a given area is generally muchgreater than depicted in this simple example.

[0039]FIG. 6A is a diagram 600 illustrating the thermal response of anillustrative sample defect and the surrounding area. The sample defectis assumed to be a short having a resistance R of about twenty-fivethousand ohms, a volume V of the combined defect and associatedelectrodes of about 10⁻¹² m³, and an exposed surface area A of about10⁻⁵ m². The specific heat Cp of the electrodes is assumed to be about2.44×10⁶ J/m³K, and the convection heat transfer coefficient of thesurrounding air h_(air) is about 10 W/m²K. For an applied power of about6 milliwatts the equilibrium temperature at the defect location is about6.5 degree C. above the initial temperature. The following heat transfermodel illustrates the thermal response of the sample defect in responseto applied power: $\begin{matrix}{{{VC}_{p}\frac{{T(t)}}{t}} = {{P_{applied}(t)} - {h_{air}{A\left( {{T(t)} - T_{air}} \right)}}}} & (1)\end{matrix}$

[0040] where:

[0041] 1. V is the volume of the combined defect and associatedelectrodes;

[0042] 2. C_(p) is the average specific heat of the defect andassociated electrodes;

[0043] 3. T(t) is temperature, in Kelvin, of the defect over time, inseconds;

[0044] 4. P_(applied)(t) is the applied power excitation over time, inseconds;

[0045] 5. h_(air) is the convention heat transfer coefficient of thesurrounding air;

[0046] 6. A is the exposed surface area of the combined defect andassociated electrodes; and

[0047] 7. T_(air) is the temperature of the surrounding air or initialtemperature (e.g., about 300K).

[0048] Equation (1) means, in essence, that the power applied to thedefect area is, at a given instant, equal to the sum of the powerabsorbed by the defect area and the power dissipated into thesurrounding environment. Initially, when the temperature differencebetween the defect area and surrounding environment is minimal, thefirst addend dominates the equation. The second addend gains influenceas the temperature of the defect area rises.

[0049] A diagram 605 illustrates the sample defect area 610 andsurrounding area 615 as a collection of boxes, each box representing theimage intensity recorded by an image pixel. For ease of illustration,diagram 605 illustrates defect area 610 as a single pixel. Diagram 600assumes about six milliwatts is applied between the source and gatelines for a time sufficient for defect area 610—a short between thesource and gate lines—to rise from an initial thermal equilibriumtemperature T_(I) of about 300K to a final equilibrium temperatureTE_(F) of about 306.5K. Under the specified conditions, traversing thistemperature window takes about 0.1-0.2 seconds for a typical activeplate.

[0050] A first response curve 620 illustrates the thermal response ofdefect area 610. The vertical axis of diagram 600 represents temperatureas a percentage of the temperature span between initial temperatureT_(I) and final equilibrium temperature TE_(F) of response curve 620.The horizontal axis represents time, in thermal time constants τ. Thethermal time constant τ is the time required for the temperature ofdefect area 610 to rise 63.2% of the way from a given temperature tofinal equilibrium temperature TE_(F). For practical purposes, the defecttemperature is at the final equilibrium temperature TE_(F) after four orfive time constants τ.

[0051] The thermal response of area 615 surrounding defect area 610differs from the thermal response of defect area 610. If the defect is ashort, heat from area 610 diffuses into area 615, causing thetemperature of area 615 to rise with defect 610. The rising temperatureof area 615 lags that of defect 610, however, and rises to a lower finalthermal equilibrium temperature than defect 610. Test vectors applied inaccordance with some embodiments provide increased temperature contrastbetween areas 610 and 615 to allow IR imaging systems to more easilyresolve defect features. IR inspection systems then employ uniqueimage-acquisition timing to capture test image data well before defectarea 610 reaches final equilibrium temperature TE_(F). (If defect area610 is an open, the temperature of area 610 lags surrounding area 615,but nevertheless eventually reaches a final equilibrium temperature.)

[0052]FIG. 6B includes a diagram 630 illustrating test vectors andimage-acquisition timing that enhance thermal contrast between defectsand the surrounding areas. Diagram 630 includes a thermal response curve640 representing the repetitive heating and cooling of defect area 610in response to test vectors. FIG. 6B additionally includes a pair ofwaveforms IMAGE and EXCITE that share a common time scale with diagram630. The high portions of waveform IMAGE represent windows of timeduring which IR images of areas 610 and 615 are captured. One or morereference images are captured during each reference window 645, whileone or more test images are captured during each test window 650. Thehigh portions 655 of waveform EXCITE represent times during which testvectors are applied to defect 610 to introduce thermal contrast betweendefect 610 and surrounding area 615.

[0053] To capture test images, an inspection system (e.g. inspectionsystem 310 of FIG. 3) applies test vectors to a device under test duringtimes 655. The inspection system then captures one or more IR images ofthe defect area well before the features of interest reach the finalthermal equilibrium temperature TE_(F).

[0054] It is desirable to keep the maximum temperature (i.e., the peaksof response curve 640) below 95% of the difference between the initialtemperature T_(I) and the final equilibrium temperature TE_(F). In somecases, the maximum temperature may even endanger DUT functionality. Theoptimal upper limit for temperature thus varies for different DUTs, testprocedures, etc., but is preferably less than 86.5% in many cases. Ourexperimental data suggest that excellent results are obtained when themaximum temperature does not exceed about 63.5% of the differencebetween the initial temperature T_(I) and the final equilibriumtemperature TE_(F) (i.e., before the passage of one time constant). Theheating/imaging steps are repeated a number of times, and the resultsaveraged or otherwise combined, to reduce the effects of noise. Themaximum and minimum peaks of response curve 640 should be sufficientlyspaced for the selected detector to resolve the temperature difference.

[0055] Defect 610 can be heated to higher temperatures, for more thanthree or four time constants, for example; in such cases, the images canstill be taken well before defect 610 reaches the final equilibriumtemperature TE_(F). Because heating takes time, selecting relatively lowmaximum temperatures speeds testing. Moreover, the test voltagesselected to maximize image contrast may, if applied too long, raise thetemperatures of areas 610 and 615 high enough to damage sensitivecomponents. In such cases, the test vectors are applied long enough toachieve a desired level of thermal contrast without raising thetemperatures of areas 610 and 615 above some maximum temperature.

[0056] The applied test vectors differ depending in part on the DUT. Inone embodiment suitable for testing an active panel from aliquid-crystal display (LCD) having SXGA resolution, the low portions ofthe EXITE waveform represent periods of about 20 ms when no test vectorsare applied, and the high portions 655 represent periods of about 80 msduring which time AC source and common test vectors oscillate from zeroto 30 volts at about 70 KHz.

[0057] Conventional infrared imaging systems represent images usingarrays of numbers, each number representing a pixel intensity value.Each pixel intensity value, in turn, represents the image intensity of acorresponding area of the DUT. In inspection system 310 of FIG. 3, forexample, frame grabber 325 delivers to computer 320 an array of pixelintensity values for each captured image.

[0058] Some conventional inspection systems average sequences of imagesto reduce the effects of noise. Averaged test images are then contrastedwith a reference image to identify differences, which indicate thepresence of defects. The above-described methods and systems can be usedto produce enhanced test and reference images.

[0059] One embodiment employs an image transformation in place ofconventional averaging. The image transformation, defined below inequation 2, is applied to both a sequence of test images and a sequenceof reference images. The resulting transformed test and reference imagesI_(T) and I_(R) are then contrasted to identify differences.$\begin{matrix}{I = {D\left( {L\left( {F\left( \frac{\sum\limits_{i = 0}^{n - 1}\quad {D^{- 1}\left( I^{i} \right)}}{n} \right)} \right)} \right)}} & (2)\end{matrix}$

[0060] where:

[0061] 1. D⁻¹ represents an image transformation (casting) fromsixteen-bit numbers provided by frame grabber 325, each numberrepresenting the intensity of one pixel, to their floating-pointinverses (quasi-inverse to D, below);

[0062] 2. I^(i) is the i-th image of the sequence;

[0063] 3. n is the number of images in the sequence;

[0064] 4. F represents image filtering, e.g. low-pass filtering toreduce noise and eliminate data provided by defective pixels in IRdetector 315;

[0065] 5. L is the application of a look-up table to the image totranslate the range of intensity values to a different scale, e.g., adifferent range or from linear to quadratic; and

[0066] 6. D is an image transformation (casting) from floating-pointvalues to sixteen-bit numbers.

[0067] In carrying out the image transformation of equation 2 on asequence of images (test or reference), each pixel intensity value ineach of the sequence of images is converted to a floating-point number.The resulting image arrays are then averaged, on a per-pixel basis, tocombine the images into a single image array. Next, the resulting imagearray is filtered to reduce the effects of noise and to remove dataassociated with defective pixels in the imaging device. Each datumassociated with a defective pixel, identified by an extreme intensityvalue, is replaced with a new intensity value interpolated from datarepresentative of neighboring areas.

[0068] The intensity values of the combined, filtered image array areapplied to a look-up table that translates the range of intensity valuesto a different scale, e.g., a different range or from linear toquadratic. Finally, the values in the resulting translated image arrayare converted back from floating-point numbers to digital numbers toproduce the transformed image I.

[0069] The image transformation of equation 2 is applied to a series oftest images and a series of reference image to produce respectivecombined test and reference images I_(T) and I_(R). The test andreference images I_(T) and I_(R) are then contrasted, using well-knownimage processing techniques, to produce a composite image. The compositeimage highlights temperature differences between the test and referenceimages; unexpectedly warm or cool areas are indicative of defects.

[0070] In general, short circuits produce relatively high currents, andconsequently grow relatively hot. Open circuits reduce current, remainrelatively cool, and are therefore more difficult to image using IRthermography. The improved thermal contrast provided by the foregoingembodiments allows sensitive IR detectors to capture images thathighlight many types of defects previously difficult or impossible toview with conventional IR thermography. Such defects include opencircuits of the type depicted in FIG. 2. The image transformation ofequation 2 further enhances the results.

[0071]FIG. 6C depicts an experimentally obtained image 680 illustratinghow the above-described embodiments produce sufficient thermal contrastto highlight opens. A line 685 represents a relatively cool defectartifact resulting from the presence of an open.

[0072] Defect-Location Algorithms

[0073] A short between two lines increases current through those lines,and consequently elevates the temperatures of the lines. Thus the lines,though not themselves defective, may nevertheless appear in thecomposite image. The portion of the composite image highlighting thelines is termed the “defect artifact” of the short. Opens block current,and consequently reduce the temperature of associated features. Thesefeatures then appear in the composite image as a “defect artifact” ofthe open. (FIG. 6C depicts a line-type defect artifact associated withan open). Test images thus include defect data spatially correlated tothe defect region and defect-artifact data spatially correlated todefect-free regions of the imaged object. Unfortunately, defect-artifactdata can obscure defect data during image analysis, rendering itdifficult to precisely locate defects. Image-processing algorithms inaccordance with some embodiments analyze the defect data anddefect-artifact data to address this problem. (In the context of images,a related collection of defect data may be referred to as a “defect,”for brevity; likewise, defect-artifact data may be termed “defectartifacts.” Whether the term “defect” or “defect artifact” refers to aphysical feature of an imaged object or image-data representative of aphysical feature will be clear from the context.)

[0074]FIG. 7 depicts a composite image 700 showing three representativedefects, a point-type defect 705, a line-type defect 707, and acorner-type defect 710. These defects are assumed to be of similar size,but the defect images nevertheless differ due to their respective defectartifacts 715, 720, and 725. Unfortunately, actual composite IR imagesdo not so clearly distinguish defects from defect artifacts, renderingdifficult the task of precisely locating defects. FIG. 8, though notbased on measured data, more accurately depicts a composite image 800illustrating how a point-type defect 805, a line-type defect 810, andcorner-type defect 815 might appear in a composite image. The defectartifacts obscure the location of the defects. One embodiment addressesthis problem, distinguishing defects from defect artifacts to facilitatedefect detection, location, and analysis.

[0075] Image processing to distinguish a defect from the associateartifact is based, in part, on classifying defect-artifact data.Processing differs, for example, for point-type, line-type, andcorner-type defects. Embodiments of the invention therefore includeimage-processing techniques that sort defect artifacts by type.Image-processing techniques that classify artifacts by type includepattern recognition and morphological analysis. The “Handbook of Imageand Video Processing,” edited by Al Bovik (2000) and “Nonlinear Filtersfor Image Processing,” edited by E. Dougherty and J. Astola (1999)describe image processing and mathematical morphology known to those ofskill in the art and suitable for use in some embodiments: these textsare incorporated herein by reference.

[0076]FIG. 9 is a flowchart depicting a defect-location algorithm 900 inaccordance with one embodiment. Algorithm 900 receives a composite image905, optionally filtered using a fast-Fourier-transform (FFT) low-passfilter (step 907), in which defect data is bounded by defect-artifactdata. Subsequent processing automatically locates the defect data withinthe defect artifacts to produce a list of defect coordinates. In oneembodiment, composite image 905 is an IR composite image of the typediscussed above; however, many other types of images depict subjects ofinterest surrounded by artifacts of those subjects. Algorithms inaccordance with the invention can be used to localize such subjects. Forexample, images obtained from visual optics and/or nuclear-typeexperiments depict subjects and subject-artifacts.

[0077] The above-referenced Bovik reference describes a connectionbetween gray-level and binary morphology that is used in someembodiments to distinguish defects from their associated artifacts. Thisconnection is based on the observation that an image I(x),xεD can bereconstructed from the set of thresholds. Such reconstruction may beexpressed mathematically as follows:

Θ_(v)(I)={xεD:I(x)≧v},−∞>v>∞  (3)

[0078] where Θ_(v)(I) is the threshold of a grayscale composite image Iwith a threshold level v, and D⊂

² is the domain of the image:

I(x)=sup_(vεR) {xεΘ _(v)(I)}  (4)

[0079] Algorithm 900 employs a similar type of image reconstruction todefine and optimize threshold levels to yield improved defectlocalization.

[0080] A morphological filtering algorithm (MFA) 910 is applied to thefiltered composite image for each of a number of threshold levels 915.The repetition of MFA 910 for each threshold level, illustrated bybounding MFA 910 within a for-loop 920A and 920B, produces a set of ithresholded composite images 925[1 . . . i]. In each image 925, allpixel values of the filtered composite image greater than or equal tothe applied threshold level are expressed using one logic level (e.g.,logic one) and all pixel values less than threshold level 915 areexpressed using a second logic level (e.g., logic zero). In oneembodiment, threshold levels 915 are in units of standard deviation ofpixel-intensity values from a filtered composite image produced byfilter 907. Histogram analysis is provided to calculate mean p andstandard deviation g; the actual level of threshold is equal to μ+σ·T,where T is the input threshold level in units of σ. MFA 910 is detailedbelow in connection with FIG. 10.

[0081] The next for-loop (930A and 930B) treats each image 925 to asecond MFA 935, this one type-specific. For line-type artifacts, forexample, MFA 935 removes other types of defect artifacts (e.g., corner-and point-type artifacts), leaving only lines. For-loop 930 produces aset of i images, each having j line-type artifacts. In the illustration,the top image 940[1] includes two line-type defects: the point- andcorner-type defects of image 925[1] are removed. The remaining images940[2 . . . i] may have more or fewer artifacts. MFA 935 is detailedbelow in connection with FIG. 11.

[0082] In a final sequence of operations, a defect-location algorithmDLA 945 analyzes the defect artifacts in images 940 to preciselyidentify the defect coordinates 950 from among the defect artifacts. Aversion of DLA 945 specific to line-type defects is detailed below inconnection with FIG. 12.

[0083]FIG. 10 depicts an embodiment of MFA 910 of FIG. 9, which isrepeatedly applied to composite image 905 to produce a sequence of ifiltered images 925. First, one of threshold levels 915 is applied tothe FFT-filtered composite image 905 (step 1000). A morphologicalclosing step 1005 (a dilation followed by an erosion) applied to theproduct of step 1000 smoothes defect artifacts, and a subsequent filterremoves small image effects induced by noise or defective detectorpixels (step 1010). MFA 910 thus produces one of the i images 925[1 . .. i]. MFA 910 is repeated, as shown in FIG. 9, for each one of thresholdlevels 915.

[0084]FIG. 11 depicts an embodiment of type-specific MFA 935 of FIG. 9adapted for use with line-type defect artifacts. MFA 935 receivesfiltered images 925 from MFA 910 and employs type-specific filteringoperations that remove any artifacts other than line-type artifacts.Step 1110 provides type-specific morphological operations based on anartifact's position on the image rather than its geometrical properties.For example, an artifact located in areas where that type of artifact isnot expected may be eliminated. Any holes in the surviving artifacts arethen filled (step 1115) using any of a number of conventionalhole-filling techniques.

[0085] Next, step 1120 filters the images from step 1115 using atype-dependent list of constraints 1125 derived from artifact type andarea information. Constraints 1125 are a type-specific list ofmeasurable artifact parameters and ranges. For example, line-typeartifacts in simple cases may be filtered out by their circular factor(“lines” are not circular), orientation (e.g., line-type artifacts mustbe close to vertical or horizontal), and their edge intersections (e.g.,line-type defects, distinct from corner-type defects, do not interesttwo adjacent image boundaries). The resulting filtered binary image 940contains only the desired type of artifact. In one embodiment, image 940has two pixel values: background (0) and artifact (1). The artifactsappear as areas of touching pixels, all set to 1; the surrounding areasappear as pixels set to 0. The details of the implementation of eachblock of MFA 900 are well known to those of skill in the art of imageprocessing. MFA 935 is repeated, as shown in FIG. 9, for each one ofthreshold levels 915.

[0086] The set of morphological operations applied in MFA 935 isspecific to line-type defects in the example, but these operations canbe modified as desired to accommodate e.g. point- and corner-typedefects. If only point-type artifacts were of interest, MFA 935 can beadapted to remove the artifacts that touch the border of the image,artifacts greater than a specified maximum area, etc., which wouldcorrespond to line-, corner-, and other type artifacts. If onlycorner-type artifacts were of interest, MFA 935 can be adapted to removeartifacts that do not represent intersecting perpendicular lines of aspecified minimum area, etc. MFA 935 can be modified to select these andother types of artifacts using well-known image-processing techniques.

[0087]FIG. 12 depicts an embodiment of defect-location algorithm (DLA)945 of FIG. 9, which generates a list of physical coordinates fordefects on an object under test using the array 940 of type-specificimages. DLA 945 is specific to line-type defect artifacts, but can beadapted for use with other types of defects, as will be evident to thoseof skill in the art.

[0088] In the first step, a line-defect filter LDF algorithm 1200produces an array of i LDF structures 1205[1 . . . i], one LDF structurefor each image 940. In addition to type-specific images 940, LDFalgorithm 1200 receive as inputs 1210 the original filtered compositeimage (from step 907 of FIG. 9), the value of the initial thresholdlevel used to create images 940, a threshold step value indicative ofthe difference between threshold values used to acquire successiveimages, and the number i of threshold levels (the number of thresholdlevels i is the same as the number of images 940 because 940 arethresholded images).

[0089]FIG. 13 depicts a linked-list array of LDF array structures 1205(FIG. 12) in accordance with one embodiment. In this example, fourthreshold levels and associated processing are used to produce four LDFstructures 1205[1] through 1205[4], though the actual number of arrayscan be more or fewer. These arrays are adapted for use with line-typedefect artifacts, but can be modified for use with other types. Each LDFstructure 1205[i] includes the below-listed features.

[0090] 1. A threshold field 1300 that stores the threshold level appliedto the composite image to produce the respective binary image 940[i].

[0091] 2. A number field 1305 that stores the number n of identifieddefects, two in the example of FIG. 12.

[0092] 3. An image field 1310 that stores the respective binary image940[i].

[0093] 4. An array 1315 of defect structures 1320[1−j], where j is thenumber of defect artifacts in the respective image 940[i]. Each defectstructure 1320 stores the X and Y coordinates of the pixel at the tip,or “peak,” of the respective line-type defect artifact (defects areassumed to be near the tip of line-type defect artifacts). Each defectstructure additionally includes a pixel-value field 1325 that stores thepixel intensity value corresponding to the X and Y coordinates of thepeak pixel in composite image 905.

[0094] 5. An array 1330 of line structures 1335[1−j], each associatedwith a defect artifact in the respective filtered image 940[i]. Eachline structure 1335 includes an area field 1340 that stores the area ofthe defect artifact (in pixels); a rectangle field 1345 that stores theleft, top, right, and bottom coordinates of a rectangle encompassing thedefect artifact; a belongs-to field 1350 that stores a line indexrelating a line-type artifact to features of an image taken at a lowerthreshold level (−1 of no such line exists); and a contains field 1355that stores an array of line indices relating a line-type defect in thefiltered image with one or more related lines in another filtered imagetaken at a higher threshold level. The purposes of Belongs-to field 1350and contains field 1355 are detailed below in connection with FIGS. 14and 15A-D.

[0095] Returning to FIG. 12, a LDF structures 1205[1 . . . i] areanalyzed (step 1210) to develop j LDF gradient peak profiles 1215[1 . .. j], one for each type-specific defect artifact in each image 940. Thefollowing discussion of FIGS. 14, 15A-15D, and 16 illustrates theprocess of peak profiling 1210.

[0096]FIG. 14 depicts an illustrative filtered composite image 1400similar to what one might expect from step 907 of FIG. 9. Image 1400includes a pair of vertical line-type defect artifacts A1 and A2 and apoint-type artifact A3. The boundaries of the defect artifacts areblurred to depict the lack of an emphatic image boundary between defect-and defect-artifact data. It is assumed, however, that defectsassociated with line-type defect artifacts are near artifact tips, or“peaks,” and defects associated with point-type defects are centeredwithin the related artifact.

[0097] FIGS. 15A-15D depict binary, type-specific images 1505, 1510, and1515 like images 940[1 . . . i] of FIG. 9. Each image representscomposite image 1400 with a distinct applied threshold value, per MFA910; point-type artifact A3 is removed from each binary image by thesubsequent application of type-specific MFA 935. Each image is stored infield 1310 of an LDF structure 1205 (FIG. 13) along with the otherimage- and artifact-specific information described above in connectionwith FIG. 13.

[0098] Image 1500 (FIG. 15A) includes a pair of line artifacts LA1 andLA2. A related key 1520 identifies artifacts LA1 and LA2 ascorresponding to respective artifacts A1 and A2 of FIG. 14. As thresholdlevels increase, line-type artifacts become smaller and thinner, and maydisappear or split into several disconnected lines. In FIG. 15B, forexample, the defect artifact LA2 depicted as a single line in FIG. 15Aappears as a pair of line artifacts LB2 and LB3. A key 1525 identifiesartifact LB1 as belonging to artifact LA1 of image 1500 and artifactsLB2 and LB3 as belonging to artifact LA2 of image 1500. This artifact“ownership” is recorded in the “belongs to” field 1350 of the LDF arrayassociated with image 1505; similarly, the “contains” field 1355 of theLDF array associated with image 1500 notes that artifact LA1 “contains”artifact LB1 and artifact LA2 contains artifacts LB2 and LB3. FIGS. 15Cand 15D include different collections of defect artifacts and respectivekeys 1530 and 1535 illustrating the “belongs to” relationship betweenartifacts in the various images.

[0099]FIG. 16 depicts a tree 1600, a compilation of the information inkeys 1525, 1530, and 1535 of FIGS. 15B-D. Tree 1600 illustrates therelationship between defect artifacts A1 and A2 and the line-typeartifacts in binary images taken using successively greater thresholdlevels. For example, artifact LD2 of image 1515 belongs to artifact LC3of image 1510, which belongs to artifact LB3 of image 1505, whichbelongs to artifact LA2 of image 1500. Likewise, artifact LD1 of image1515 belongs to artifact LC1 of image 1510, which belongs to artifactLB1 of image 1505, which belongs to artifact LA1 of image 1500.

[0100] Returning briefly to FIG. 12, LDF gradient peak profiling step1210 uses the data depicted in FIGS. 15A-15D to produce j peak profiles1215[1 . . . j], one profile for each defect. FIG. 17 is an illustrativepeak profile 1700 showing the relationship between four defect artifacts1705, 1710, 1715, and 1720, all of which relate to a common defectartifact. Profile 1700 expresses a relationship between the thresholdlevel and the placement of the peak pixel in each defect artifact alongthe y image axis. A plot 1725 of Pixel intensity along a slice of pixelsin parallel with a bisecting the line-type defect artifact of agrayscale image illustrates how pixel intensity generally increases asthe defect artifact is encountered. Artifacts 1705, 1710, 1715, and 1720represent slices of plot 1725 taken at four threshold levels. The bold“+” signs indicate the y locations of the peak (tip) pixels of therespective defect artifacts for different threshold levels.

[0101] Returning to FIG. 12, locating step 1220 extracts from the LDFarrays data of the type represented in FIG. 17 to produce a number oftwo-dimensional arrays, each with first dimension DIMi (the number ofthreshold levels) and second dimension DIMj (the number of artifacts inthe image thresholded at the lowest level, it is the size of array 1320of LDF structure 1205[1]). These arrays are as follows (the formaldefinitions are given below in pseudocodes:

[0102] 1. y[j,i] is y-coordinate of defect structure 1320[j] of LDFstructure 1205[i], it specifies the defect related to the j-th artifactof the image 1310 thresholded at the level i;

[0103] 2. Line[j,i] is the index of the line that contains defect j atthreshold level i (pseudocodes explain how the index is chosen; and

[0104] 3. dy[j,i] is the difference between y values of defects 1320[j]from LDF structures 1205[i+1] and 1205[i].

[0105] For every artifact j, step 1515 performs the followinginitialization and loop:

[0106] Initialization: (for every j=1, . . . , Dimj):

[0107] Line[j,1]=j;

[0108] y[j,1]=LDF_Array[1].Defects[j].y

[0109] At threshold level #1 (initial threshold) all filtered lines areassumed to contain defects, so Line[j,1] is the index of the line andy[j,1] is the assumed y position of the defect associated with thisline.

[0110] Iteration i: i=1, . . . , DIMi−1 (for every j)

[0111] /* i-threshold index; j-index of the line 1335 from LDF structure1205[1] corresponding to the lowest threshold level.

[0112] */

[0113] 1. k_(opt)=argmin_(k){

[0114] LDF_Array[i+1].

[0115] Defects[

[0116] LDF_Array[i].Lines[Line[j,i]].Contains[

[0117] k]].y,

[0118] }

[0119] 2. Line[j,i+1]=LDF_Array[i].Lines[Line[j,i]].

[0120] Contains [k_(opt)]

[0121] 3. y[j,i+1]=LDF_Array[i+1].Defects[Line[j,i+1]].y

[0122] 4. dy[j,i]=y[j,i+1]−y[j,i]

[0123] In steps 1 and 2 above, for a chosen line[i,j] of threshold leveli, the line for the next threshold level (i+1) belongs to line[j,i].Among the lines of the threshold level (i+1) belonging to line[j,i], theline with the lowest y value is chosen as the line most likely toinclude the defect (the y axis of each image goes from top to bottom, sothe lowest y value represents the highest point). After choosing theline Line[j,i] with the lowest y value, the y value for the defectassociated with this line is taken from the corresponding linestructure. The value dy[j,i] defined at step 4 of the foregoingpseudocode is used below to define the LDF gradient

[0124] When line defects have been presented by trees of artifacts, asdepicted in FIG. 16, and LDF gradient peak profiling has been made, theends of the lines associated with defects (upper ends according toassumption made above) are defined as maxima of LDF gradients. The LDFgradient for each threshold level index i is defined by $\begin{matrix}{{{{LDF}{Grad}}\left\lbrack {j,i} \right\rbrack} = \frac{D_{Th}}{{dy}\left\lbrack {j,i} \right\rbrack}} & (5)\end{matrix}$

[0125] where j is the defect index, i is the threshold level index, andD_(Th) is the separation between threshold levels. D_(Th) is a constant,so the following equality holds: $\begin{matrix}{{\max_{i}{{{LDF}{Grad}}\left\lbrack {j,i} \right\rbrack}} = \frac{D_{Th}}{\min_{i}{{dy}\left\lbrack {j,i} \right\rbrack}}} & (6)\end{matrix}$

[0126] Further, because only the location of the maxima of LDF Gradientsis used, the task of finding the maxima of LDF gradients is equivalentto searching for the minimal elements in the rows of array dy[ ]calculated by the LDF gradient peak profile algorithm 1210. In manycases the minimal dy is zero because the image location is measured innumbers of pixels, and with this level of granularity the y-location ofthe defect may be the same for neighboring threshold levels.

[0127] Sometimes there are several locations of minimal dy. Themultitude of locations with maximal LDF gradients (minimal dy) requirescalculation of the first and the last indexes of the minimal elements inthe rows of array dy[ ]. The data structure of the type maxLDFGradcontains the following elements:

[0128] 1. Threshold is the threshold level of the maximal LDF gradient;

[0129] 2. Th.Ind. is index i of the threshold level;

[0130] 3. Line Ind. is the line index j associated with the defect atthis threshold level;

[0131]4. DY is the minimal value of the row of array dy[ ]; and

[0132] 5. x and y are the coordinates of defect location.

[0133] Step 1220 employs an analysis of LDF gradient peak profiles 1215to locate the defects associated with the line-type defect artifactsspecified in the line structures of the LDF array. Step 1220 performs acalculation based on First and Last maxLDFGrad structures (input data1225) corresponding to the first and the last indexes of the minimalelements in the rows of array dy. One or both structures are used toderive the corresponding defect location. The location can be defined as(x,y) from defect structure 1320[j] of LDF structure 1205[i] where j andi are taken from either First or Last maxLDFGrad structure. In thedefault case the defect location is midway between these two. Theresulting list of defect locations 1230 accurately locates defectswithin line-type defect artifacts in the image.

[0134] The final step 1235 employs fiducial coordinates 1240 totranslate the image coordinates 1230 into physical defect coordinates950 (FIG. 9). For this purpose, the object under test is assumed to bealigned with the respective detector using fiducial marks, usually twoof them.

[0135] The set of operations applied in DLA 945 is specific to line-typedefects in the example, but these operations can be easily modified asdesired to accommodate e.g. point- and corner-type defects.Defect-location algorithm 900 (FIG. 9) therefore locates various typesof defects within their respective artifacts.

[0136] While the present invention has been described in connection withspecific embodiments, variations of these embodiments will be obvious tothose of ordinary skill in the art. For example, the methods and systemsdescribed above can be applied to many types of electrical circuits,including integrated circuits, printed-circuit boards,micro-electromechanical systems (MEMS), semiconductor wafers, and somebiomedical specimens. Moreover, while the IR inspection systemsdescribed above employ a combination of electrical excitation andthermography that produces thermal defect artifacts, other types ofinspection systems employ other types of excitation and/or imaging, andconsequently produce other types of defect artifacts. For example, someimaging systems acquire images using white light, combined spectroscopy,or magnetic fields. The embodiments described above for isolatingdefects from defect artifacts are not limited to IR inspection systemsof the type described above. Therefore, the spirit and scope of theappended claims should not be limited to the foregoing description.

What is claimed is:
 1. A test configuration comprising: a. a displaypanel including: i. a plurality of source lines; ii. a plurality ofcontrol lines; iii. a plurality of common lines; iv. a two-dimensionalarray of display elements, each display element including a transistorand a capacitor, wherein the transistor has a first current-handlingterminal connected to one of the source lines, a second current-handlingterminal, and a control terminal connected to one of the control lines,and wherein the capacitor has a first capacitor terminal connected tothe second current-handling terminal and a second capacitor terminalconnected to one of the common lines; b. an infrared detector positionedto receive infrared radiation from the display panel; and c. a signalgenerator having: i. a first test-signal output terminal connected to atleast one of the source lines; ii. a second test-signal output terminalconnected to at least one of the control lines; and iii. a thirdtest-signal output terminal connected to at least one of the commonlines.
 2. The test configuration of claim 1, wherein the signalgenerator simultaneously provides a first test vector on the first testvector output terminal, a second test vector on the second test-signaloutput terminal, and a third test vector on the third test-signal outputterminal.
 3. The test configuration of claim 2, wherein at least one ofthe first and third test vectors is an AC signal.
 4. The testconfiguration of claim 3, wherein the second test vector is a DC signalbiasing on the transistor.
 5. The test configuration of claim 2, whereinthe second test vector turns the transistor on.
 6. The testconfiguration of claim 2, wherein at least one of the first and thirdtest vectors is of a test frequency and encounters a resistance and acapacitance, and wherein the test frequency is matched to thecapacitance and the resistance to provide maximum power transfer fromthe signal generator to the panel.
 7. The test configuration of claim 1,wherein the signal generator applies a test vector across the source andcommon lines to develop a test current through the first and secondcurrent-handling terminals of the transistor.
 8. A test configurationcomprising: a. an electrical circuit having at least one defect disposedwithin a defect region, the defect region having an initial temperature;b. an infrared detector positioned to receive infrared radiation fromthe defect region; and c. a signal generator having at least onetest-signal output terminal connected to the electrical circuit, thesignal generator applying a test vector to the defect via the electricalcircuit, wherein the defect exhibits a thermal response to the appliedtest vector; d. wherein the infrared detector captures an image of thedefect after application of the test vector and before the defectreaches 95% of a difference between the initial temperature and a finalequilibrium temperature.
 9. The test configuration of claim 8, whereinthe infrared detector captures the image before the defect reaches about86.5% of the difference.
 10. The test configuration of claim 8, whereinthe infrared detector captures the image before the defect reaches about63.5% of the difference.
 11. The test configuration of claim 8, whereinthe signal generator includes a second test-signal output terminal, theelectrical circuit comprising a transistor having a firstcurrent-handling terminal connected to the first-mentioned test-signaloutput terminal, a control terminal connected to the second test-signaloutput terminal, and a second current-handling terminal.
 12. The testconfiguration of claim 11, wherein the test vector is an AC signal. 13.The test configuration of claim 12, wherein the signal generator appliesa second test vector to the control terminal via the second test-signaloutput terminal.
 14. The test configuration of claim 13, wherein thesecond test vector turns the transistor on.
 15. The test configurationof claim 11, wherein the signal generator includes a third test-signaloutput terminal, the test configuration further comprising a capacitanceconnected between the second current-handling terminal of the transistorand the third test-signal output terminal.
 16. A method for identifyinga defect on an electrical circuit, the method comprising: a. applyingtest vectors, at a first instant, to the defect via the electricalcircuit, wherein the defect exhibits a thermal response to the testvectors, the thermal response being characterized by a thermal timeconstant; and b. capturing a test image of the defect at a secondinstant separated from the first instant by less than three timeconstants.
 17. The method of claim 16, wherein the second instant isseparated from the first instant by less than two time constants. 18.The method of claim 16, wherein the second instant is separated from thefirst instant by less than one time constant.
 19. The method of claim16, further comprising repeating steps (a) and (b) at least twice tocapture a plurality of test images.
 20. The method of claim 19, furthercomprising capturing a reference image between ones of the plurality oftest images.
 21. An automated method of locating at least one defect onan object of interest, wherein the object includes at least one defectregion corresponding to the defect and a defect-free region absent thedefect, the method comprising: a. acquiring an image of the object, theimage including image data spatially correlated to the object, the imagedata including: i. defect data spatially correlated to the defectregion; and ii. defect-artifact data spatially correlated to portions ofthe defect-free region; and b. analyzing the defect-artifact data. 22.The method of claim 21, further comprising classifying thedefect-artifact data as a member of one of a plurality of artifacttypes.
 23. The method of claim 22, wherein the artifact types include atleast one of point-type, line-type, and corner-type.
 24. The method ofclaim 21, further comprising filtering the image data to remove at leastone type of defect, thereby creating a type-specific image.
 25. Themethod of claim 21, further comprising: a. applying a first thresholdvalue to the image data to create a first thresholded image, the firstthresholded image depicting a first defect area in the defect region anda first defect-artifact area encompassing the first defect area andextending into the defect-free region; and b. applying a secondthreshold value to the image data to create a second thresholded image,the second thresholded image depicting a second defect area in thedefect region and a second defect-artifact area encompassing the seconddefect area and extending into the defect-free region.
 26. The method ofclaim 25, further comprising selecting a first peak within the firstdefect-artifact area, selecting a second peak within the seconddefect-artifact area, and calculating a gradient extending between thefirst and second peaks.
 27. The method of claim 26, further comprisingcalculating the defect coordinates using the gradient.
 28. The method ofclaim 25, wherein the first and second defect areas correspond spatiallyto the defect region.
 29. An imaging systems for localizing a defect onan object under test, the imaging system comprising: a. a excitationsource applying a test vector to the defect, wherein the defect exhibitsa response to the applied test vector; b. an image detector positionedto observe the response of the defect, thereby producing an imageincluding defect data and defect-artifact data encompassing the defectdata; and c. an image processor analyzing the defect-artifact data andthe defect data to locate the defect data within the defect-artifactdata.
 30. The imaging system of claim 29, wherein the response is athermal response.
 31. The imaging system of claim 29, wherein analyzingthe defect-artifact data comprises: a. applying a plurality of thresholdvalues to the image to produce a plurality of thresholded images; b.wherein a number of the thresholded images include respective defectartifacts corresponding to the defect region.
 32. The imaging system ofclaim 31, further comprising a type-specific filter sorting the defectartifacts in each thresholded image by type to produce type-specificimages having type-specific defect artifacts.
 33. The imaging system ofclaim 32, wherein the image processor includes a data structureassociating type-specific defect artifacts from different type-specificimages.
 34. The imaging system of claim 33, wherein the image processorestablishes a gradient between peak values of the type-specific defectartifacts.
 35. The imaging system of claim 34, wherein the imageprocessor calculates the location of the defect using the gradient. 36.An inspection system comprising: a. an object under test, including atleast one defect; b. a means for heating the defect and relatedresources; c. an infrared imaging device capturing a thermal imageheated object, the thermal image including defect data representative ofthe defect and defect-artifact data representative of the relatedresources; and d. image-processing means for locating the defect datawithin the defect-artifact data.